State Of Charge Estimation Algorithm For Vehicle Power Battery Management System

The emergence of automobile not only promotes the progress of society,but also promotes the economic development of various countries,and also brings convenience to people’s life.However,in recent years,with the rapid growth of car ownership in various countries,the problems of energy crisis and environmental pollution have become increasingly prominent.In the face of these problems,the emergence of new energy vehicles can not only reduce CO₂ emissions,but also reduce the dependence on non-renewable energy.Therefore,countries around the world have begun to vigorously support and invest in the new energy industry,and new energy vehicles have sprung up around people.The development of new energy vehicles depends on the progress of lithium-ion battery technology,in which power lithium battery is one of the core technologies of pure electric vehicles,so it is imperative to develop a battery management system.In battery management system,battery state of charge estimation is the focus of current research.Based on the identification of lithium-ion battery parameters by immune genetic algorithm,this paper proposes a lithium-ion battery state of charge estimation method based on extended Kalman particle filter.Firstly,an immune genetic algorithm is designed to identify the parameters of the second-order RC equivalent circuit model of lithium-ion battery.The parameter identification in HPPC test shows that the mean square error of Ig A algorithm is reduced from 1.09x10^-5 to 5.45x10^-6,and the calculation time is reduced from 4.25s to 2.64s,compared with GA algorithm,which can calculate the basic parameters of the second-order equivalent circuit model efficiently and accurately.At the same time,the extended Kalman filter and particle filter are combined to design an extended Kalman particle filter algorithm for lithium-ion battery state of charge estimation.Aiming at the nonlinear and time-varying characteristics of lithium-ion battery system,immune genetic extended Kalman particle filter can improve the calculation accuracy and the stability of state estimation.IGEKPF algorithm and the second-order RC equivalent circuit model are built by using Matlab/Simulink software.On the experimental platform,the adaptability and robustness of immune genetic extended Kalman particle filter are verified by experiments.In UDDS and the Economic Commission for Europe,The average estimation error of the extended Kalman filter algorithm is 1.63%and 1.06%respectively,compared with the extended Kalman particle filter algorithm of 0.65%and 0.43%,which shows that the extended Kalman particle filter algorithm is a good algorithm for lithium-ion battery charging state estimation.